Open Access

ARTICLE

Task-Based Resource Allocation Bid in Edge Computing Micro Datacenter

by Yeting Guo¹, Fang Liu², Nong Xiao¹, Zhengguo Chen¹

1 College of Computer, National University of Defense Technology, Harbin, 410073, China.
2 School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China.
3 Computer Science Department, University of Pittsburgh, Pittsburgh, 15213, USA.

*Corresponding Author: Fang Liu. Email: .

Computers, Materials & Continua 2019, 61(2), 777-792. https://doi.org/10.32604/cmc.2019.06366

Abstract

Edge computing attracts online service providers (SP) to offload services to edge computing micro datacenters that are close to end users. Such offloads reduce packet-loss rates, delays and delay jitter when responding to service requests. Simultaneously, edge computing resource providers (RP) are concerned with maximizing incomes by allocating limited resources to SPs. Most works on this topic make a simplified assumption that each SP has a fixed demand; however, in reality, SPs themselves may have multiple task-offloading alternatives. Thus, their demands could be flexibly changed, which could support finer-grained allocations and further improve the incomes for RPs. Here, we propose a novel resource bidding mechanism for the RP in which each SP bids resources based on the demand of a single task (task-based) rather than the whole service (service-based) and then the RP allocates resources to these tasks with following the resource constraints at edge servers and the sequential rule of task-offloading to guarantee the interest of SPs. We set the incomes of the RP as our optimization target and then formulate the resource allocation problem. Two typical greedy algorithms are adopted to solve this problem and analyze the performance differences using two different bidding methods. Comprehensive results show that our proposal optimizes resource utilization and improves the RP’s incomes when resources in the edge computing datacenter are limited.

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Citations